In today’s scenario, social media has grown rapidly and widely. It has become a common source of news, and people rely on it for the newest information, current stories, and interpersonal communication. Many individuals prefer social media and web portals for news searches and reading instead of traditional newspapers, and online social media has changed the way people talk and share content with each other. But some of the things shared on social media may not be true and could be meant to trick or mislead people for personal or commercial purposes, influencing government policies, and for many other benefits. Such information is often referred to as fake news. To tackle this issue, fake news detection has gained significant attention. However, many traditional fake news detection models rely on handcrafted features and machine learning techniques as well as utilize the potential of deep learning models. Hence, the model learns how to operate by distinguishing between the fake information or data that should be discarded and the fake information or data that should be retained. The LSTM network routes information through three gates named input gate it, output gate ot, and forget gate ft. These gates are incorporated into the LSTM network’s cell. The proposed technique provided an accuracy of 96.48%, recall of 99.45%, precision of 99.32%, and F1-score of 95.78%.